Telemedicine in Diabetic Retinal Screening: Pre- and Post-COVID-19 Challenges a New Perspective

Abstract

Abstract Telemedicine in ophthalmology has been around for decades and has been successful with its use in diabetic retinal screening in countries like the UK (with the introduction of the UK National Diabetic Eye Screening Programme in 2003). However, most telemedicine, in the field of diabetic retinopathy, has largely been reliant on human graders for triage purposes. With the advent of COVID-19, patients with chronic conditions, such as diabetes, were disproportionately affected. The pandemic also caused significant rise in patients on waiting lists. Before the pandemic, there have been studies illustrating the use of artificial intelligence (AI) to analyse images obtained from patients screened for monitoring of their diabetic retinopathy. The image analysis by AI and deep-learning algorithms offers insight into the future of screening in diabetes. The transition, from the use of human graders in teleophthalmology to the use of AI-based image analysis has the potential to screen a wider cohort of patients, thereby tackling waiting lists awaiting screening which has lengthened since after COVID-19. It is therefore vital to understand the role of AI in screening diabetic retinopathy patients, from a patient-acceptability, cost-effectiveness and reliability perspective as, this offers potential answers to streamline the screening process further.